System and method for predicting behavior and outcomes

ABSTRACT

A system and method for predicting behavior and/or outcomes related to a consumer&#39;s experience with an organization are implemented. Household data for households that are associated with a customer service interaction as of a certain date is collected, the household data having been created over a first pre-determined period of time preceding the certain date. The household data is analyzed to identify positive household data sets and negative household data sets. The positive household data sets relate to customer service interactions which preceded a high level customer service interaction within a subsequent period of time and the negative household data sets relate to customer service transactions which did not precede a high level customer service interaction with the subsequent period of time. The positive household data sets and the negative household data sets are processed in the aggregate, using a trained support vector machine model, to determine cumulative differences between data contained within the positive household data sets and the negative household data sets. Each day, daily household data is collected. The daily household data describes individual customer service transactions occurring during a previous calendar day. The daily household data is processed using the model to determine whether each individual customer service transaction occurring during the previous calendar day is more similar to the positive household data sets or to the negative household data sets. The individual customer service transactions that are more similar to the positive household data sets are flagged for proactive intervention.

FIELD OF THE INVENTION

The invention relates to models for predicting behavior and/or outcomesrelated to a consumer's experience with an organization.

BRIEF SUMMARY OF THE INVENTION

In accordance with embodiments of the present invention, a system andmethod for predicting behavior and/or outcomes related to a consumer'sexperience with an organization are implemented. Household data forhouseholds that are associated with a customer service interaction as ofa certain date is collected, the household data having been created overa first pre-determined period of time preceding the certain date. Thehousehold data is analyzed to identify positive household data sets andnegative household data sets. The positive household data sets relate tocustomer service interactions which preceded a high level customerservice interaction within a subsequent period of time and the negativehousehold data sets relate to customer service transactions which didnot precede a high level customer service interaction with thesubsequent period of time. The positive household data sets and thenegative household data sets are processed in the aggregate, using atrained support vector machine model, to determine cumulativedifferences between data contained within the positive household datasets and the negative household data sets. Each day, daily householddata is collected. The daily household data describes individualcustomer service transactions occurring during a previous calendar day.The daily household data is processed using the model to determinewhether each individual customer service transaction occurring duringthe previous calendar day is more similar to the positive household datasets or to the negative household data sets. The individual customerservice transactions that are more similar to the positive householddata sets are flagged for proactive intervention.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofembodiments of the invention, will be better understood when read inconjunction with the appended drawings of an exemplary embodiment. Itshould be understood, however, that the invention is not limited to theprecise arrangements and instrumentalities shown.

In the drawings:

FIG. 1 illustrates an exemplary method of the present invention; and

FIGS. 2 and 3 illustrate exemplary systems that may be used to carry outthe methods of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Customer case escalation involves repeated interactions between acustomer and customer service representatives of an organization.Customers become frustrated due to increasing time and emotionalinvestment during the resolution process. Expenditure of time andemotion decreases customer satisfaction and, possibly, customer tenurewith an organization. In accordance with the systems and methodsdescribed herein, using a predictive model, customers are identified whoare most likely to experience prolonged and lengthy escalationprocedures when contacting an organization about an issue they arehaving with the products and/or services of the organization. Onceidentified, business and case details pertaining to these individualscan be directed to specialists within the organization for outboundintervention with the intention of circumventing the normal escalationprocess. This is valuable to an organization because most customers whoendure the complete escalation/resolution process experience lowsatisfaction following the experience. Thus, use of the presentinvention saves customer time and frustration and can result in anincrease in both satisfaction and retention among this set of vulnerablecustomers by facilitating early intervention by specializedrepresentatives who can efficiently resolve complex issues. This resultsin reduced resolution time for an organization for complex cases.

The model incorporates business details that are unique to the customer(e.g., customer history, product details, and contact history, by way ofexample) and log notes collected during the calls to the customerservice representatives by the customer. The model applies the data toclassify and rank individuals based on their relative similarity betweena novel individual and the characteristics of individuals who havebehaved in a particular way and experienced a particular outcome,namely, moved through the escalation process and reached a higher levelof customer service within an organization within a certain number ofdays of initial contact with a customer service representative. Suchhigher level of service within the organization is one that resolvesserious complaints and problems, which has representatives that aretrained to resolve complex issues on behalf of customers quickly (e.g.,within 48 hours). Generally, a group within an organization that has oneor more of these characteristics is referred to herein as the office ofconsumer advocacy (OCA), for ease of reference.

Machine learning is used to train a classification model (e.g., a linearsupport vector machine) using the organization's aggregated customerdata. More particularly, the model relies on both aggregated datareflecting the organization's customers' behavior, as well as data thatis unique to the consumer, to predict the behavior of the customer andrelated outcome (e.g., how likely is it that the customer will end uphaving his problem escalated to OCA and, if there is intervention beforethat occurs, what impact will that have on customer retention).

The invention solves the problem of identifying customers who are likelyto experience prolonged escalation during their case resolution processand provides the opportunity to intervene early in the process for thepurpose of remedying the problem at hand and creating a better consumerexperience. Existing solutions use predefined business parameters andnarrowly defined text analysis (e.g., key word searches) in an attemptto predict why a customer is calling, what the customer may purchase orwhat the customer's sentiment may be. However, such existing solutionsdo not employ a methodology that involves predicting future actions ofcustomers based on aggregate, individual-level assessment ofcomprehensive business details, an individual's past interactions withan organization and the unstructured and qualitative aspects of thoseinteractions.

The machine learning component of the invention is now described. In theexemplary embodiment, a linear support vector machine (SVM) is employed.Machine learning identifies patterns in customer characteristics. Usinga classification model and text analytics, the approach predicts caseescalation by uncovering patterns in case data, the text of customerservice representative logs, and customer behavior. In accordance withthe approach, past behaviors and characteristics of customers who haveexperienced escalation and OCA contact is examined. The patterns inthese behaviors are modeled to predict who may need intervention.Individual elements were found to confer little predictive power;rather, an analysis of the combination of factors is necessary foreffective employment of the model.

In one exemplary application, households that include customers of theorganization with at least one OCA complaint during the last year areidentified. A history of transactions between the organization and amember of each household is collected from the organization's database.Using this data, the classification model learns the features oftransactions that are associated with OCA contact. For example, thevariables considered in connection with the analysis may include:

(1) contact frequency—number of transactions, by category, by timeperiod over a certain historical period (e.g., 9 months or a year);number of transactions in the last month; number of transactions in thelast week;

(2) contact reason—current business and topic categories; number of pasttransactions in each category;

(3) log notes;

(4) contact target and source—who initiated/received the currentcontact; how many past transactions were initiated/received by thecustomer/customer service representative; and

(5) content path—the paths through which the customer has contacted thecompany preceding the contact currently under consideration.

The machine learning component, thus, identifies both the quantitativeand qualitative characteristics of those who have had OCA contact in thepast. The quantitative characteristics are obtained from structured data(e.g., attributes of transactions that have occurred). The qualitativecharacteristics are obtained from unstructured data (e.g., textual datafrom log notes of customer service representatives). The unstructureddata text analysis (e.g., analysis of call logs prepared by customerservice representatives speaking with customers) enables access to thequalitative aspects of the customer experience. Unstructured textprovides a rich source of data, including data describing the emotionalstate of the consumer; details about the consumer's problem, includingwho, when and why; and elements indicating the level of complexity ofthe problem. Word count is taken into account, as larger word-countscorrelate with case complexity. Word usage is also taken into account,e.g., to identify terms used in notes of current and past transactions.Analysis of word usage may be accomplished using a document-term matrix.In accordance with the matrix, unique terms (e.g., unigrams or bigrams)that have been used more than a designated number of times in the past(e.g., 5 times) in the log notes are identified. Such terms arequantified (i.e., is the term present in the log note (binary {0, 1})and how many times). Word count data and word usage data can then beused by the model as sub-parts of a larger comprehensive relationship(i.e., is a given term present in a log note and, if so, how many timesdoes the term occur in the log note, and in connection with whatassortment and frequency of words). Model tuning and training isundertaken as part of the model development process to ensure accuracyand precision of modeling.

In the exemplary embodiment, the model uses over 2000 unique variables.Additionally, trigger mechanisms may be included in the model whichalternatively allow or disallow individuals from consideration for modelselection. For example, in order to be included in the considerationset, an individual would first have to initiate a transaction withcustomer service within certain categories (e.g. paying a bill will notresult in inclusion in the model whereas calling to provide feedbackwill place an individual in model consideration). Alternately, havingreceived outbound action (e.g., an outbound communication, such as acall from a member of the OCA, automated call, or email) within previousthree days excludes an individual from model consideration.

The object of the process is to identify individuals in need of OCAintervention. Individuals who contact customer service and reach the OCAwithin five days are used as a proxy for modeling purposes. However, inreality, many people in need of OCA contact do not reach the OCA withinfive days. Some individuals quit before reaching the OCA (despite thefact that they would have benefitted from OCA contact) and many mayreach the OCA more than five days after initial customer servicecontact. For this reason, when the model identifies a “positive” (i.e.,someone who appears very similar to those who have previously reachedthe OCA within the five days) and this individual does not, in fact,reach the OCA within five days, this does not mean that the individualdid not have the same OCA need as others that he or she resembles.

Once the model is trained, in an exemplary embodiment, all customers whocalled the organization recently (e.g., within the last day) areidentified and data regarding such transactions are input into themodel. In some embodiments, not all such transactions are considered bythe model, as some may be filtered out based on, e.g., whether anotherunit within the organization (not OCA) is already proactively contactingthe consumer. The model (trained on the data described previouslyregarding characteristics of customers who have had OCA contact in thepast) then identifies particular customers, out of all the customers whocalled the organization recently (e.g., yesterday), that are likely tohave another call with the organization about their problem. OCA maythen determine which of these identified customers would benefit fromintervention. Customers who are identified as likely to have OCA contactin the near future can be identified, and intervention employed.

Thus, the SVM learns the response variable class using labeled trainingdata (i.e., for a given transaction, was there OCA contact within acertain amount of time (e.g., 5 days) following a transaction (binary:yes or no)). The predictor variables are transaction attributes for thestructured data and textual data for the unstructured data. The SVMfinds the boundary of largest separation between classes based on thepredictor variable values.

In some embodiments, once an intervention is made, data describingfeedback resulting from the intervention may be recorded. For example,it may be recorded whether the customer found the contact useful and waspleased with the contact. This allows for direct measurement of customerimpact (i.e., does intervention improve retention) and providesparameters for model refinement (i.e., was intervention valuable and wasthe customer reception positive).

The output of the model may be displayed in a manner that allows the enduser to interact with the data.

FIG. 1 is illustrative of one embodiment of the inventive method.

In step 100, for all households with a customer service interaction asof a certain date, all available data (structured and/or unstructureddata) created over a pre-determined period of time (e.g., the 10 monthspreceding a given customer service interaction) is collected for suchhouseholds. Thus, in a simple example for ease of exposition, 10households have had a customer service interaction as of Oct. 1, 2015.All data associated with those 10 households, going back to Jan. 1,2015, is collected, including data regarding transactions carried out byor with such households and interactions with such households.

In step 110, the data collected in step 100 (i.e., the data for the 10households) is analyzed to identify household data relating to customerservice interactions (identified using a transaction ID) which precededan OCA interaction within the subsequent five days (referred to hereinas “OCA positive”) and, separately, household data relating to customerservice transactions (identified with a transaction ID) which did notprecede OCA interaction in the five subsequent days (referred to hereinas “OCS negative”).

In step 120, the trained model processes, in the aggregate, theinformation contained within the two respective data sets. Inparticular, each parameter is considered individually as well as inrelation to other available parameters within respective OCA positive orOCA negative data sets. For example, the model may give unique weightsto customer tenure (e.g., one year versus ten years), current productownership combination (e.g., auto alone versus auto, home and lifeinsurance) and the combination of tenure and product ownership (e.g.,one year of auto insurance ownership versus ten years of continuousauto, home and life insurance ownership). These three weights would beindependently factored into the output of the model. This permitscumulative differences between respective OCA positive or OCA negativedata sets to guide the classification of household data relating tonovel customer service transactions. For example, it might be observedthat customers of shorter tenure are more likely to reach the OCA thanthose of longer tenure and customers with more products are more likelyto reach the OCA than customers with fewer products, but customers ofshort tenure with many products are the most likely to reach the OCA. Inthis way, by considering the parameters of tenure and product ownershipboth independently and in relation to one another, the model is able tomake a projection of which parties are most like those who have reachedthe OCA.

In step 130, household data relating to individual customer servicetransactions within a time period, e.g., a given calendar day, areaggregated and analyzed the following day. Data sets related to each ofthe previous day's customer service transactions are then judged by themodel to be more similar to OCA positive transactions or OCA negativetransactions. In some embodiments, not all transactions are fed into themodel to be classified as being in need of intervention. In particular,for example, routine transactions or transactions associated with recentcontacts with OCA, are filtered as part of predefined model parameters.If someone has had recent contact with the OCA, the model will notrecommend additional contact. The presumption is that the OCA is workingto remedy the problem, despite possible ongoing customer action.

In step 140, customer service transactions with accompanying householddata sets that are deemed to be most similar to those which precipitatedOCA interactions in the past are flagged for proactive intervention. Insome embodiments, transactions with accompanying household data setsthat are deemed to be most similar to those which precipitated OCAinteractions are ranked based on their mathematical similarity tocharacteristics of those who have previously reached the OCA and onlythe top ranked transactions are flagged for proactive intervention.

In an exemplary embodiment, the model is deployed on an R-server(simultaneously running R versions 2.15 and 3.0) which runs on Red HatLinux (version 6.5). Daily data is pushed to the model via SPSS Modeler(version 16.0).

Exemplary computer systems, including computer hardware and software,that may be used to implement the methods of the present invention arenow described with reference to FIGS. 2 and 3 . The calculationsperformed in connection with the invention are performed by executing acomputer software application using a computer processor. The inputs forthe calculations can be received through human input into an appropriatecomputer interface. Alternatively, in some embodiments, the inputs maybe received from another computer system.

In some embodiments, the methods are carried out by a system thatemploys a client/server architecture. Such exemplary embodiments aredescribed as follows with reference to FIG. 2 . The data that may beused as an input to the system, and the outputs to the system, may bestored in one or more databases 201. Database server(s) 202 may includea database services management application 203 that manages storage andretrieval of data from the database(s) 201. The databases 201 may berelational databases; however, other data organizational structure maybe used without departing from the scope of the present invention.

One or more application server(s) 204 are in communication with thedatabase server 202. The application server 204 communicates requestsfor data to the database server 202. The database server 202 retrievesthe requested data. The application server 204 may also send data to thedatabase server 202 for storage in the database(s) 201. The applicationserver 204 comprises one or more processors 205, non-transitory computerreadable storage media 207 that store programs (computer readableinstructions) for execution by the processor(s) (e.g., to perform thecalculations described herein), and an interface 206 between theprocessor(s) 205 and computer readable storage media 207. Theapplication server 204 may store the computer programs referred toherein, as described more fully herein.

To the extent data and information is communicated over the Internet oran Intranet, one or more Internet/Intranet servers 208 may be employed.The Internet/Intranet server 208 also comprises one or more processors209, computer readable storage media 211 that store programs (computerreadable instructions) for execution by the processor(s), and aninterface 210 between the processor(s) 209 and computer readable storagemedia 211. The Internet/Intranet server 208 is employed to delivercontent that can be accessed through the communications network 212,e.g., by an end user employing computing device 213. When data isrequested through an application, such as an Internet browser, theInternet/Intranet server 208 receives and processes the request. TheInternet/Intranet server 208 sends the data or application requestedalong with user interface instructions for displaying a user interfaceon device 213.

The computers referenced herein are specially programmed to perform thefunctionality described herein.

The non-transitory computer readable storage media (e.g., 207 or 211)that store the programs (i.e., software modules comprising computerreadable instructions) may include volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information such as computer-readable instructions, datastructures, program modules, or other data. Computer readable storagemedia may include, but is not limited to, RAM, ROM, ErasableProgrammable ROM (EPROM), Electrically Erasable Programmable ROM(EEPROM), flash memory or other solid state memory technology, CD-ROM,digital versatile disks (DVD), or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer system andprocessed.

Computer system 300 comprises hardware, including a computer processor,as described more fully herein, that is specifically programmed usingcode (i.e., computer readable instructions stored in a non-transitorycomputer readable medium) to carry out the steps of the methods andcalculations described herein.

Computer system 300 includes one or more processors 301. Processor 301may be any type of processor, including but not limited to a specialpurpose or general purpose digital signal processor. Processor 301 maybe connected to a communication infrastructure 306 (for example, a busor network).

Computer system 300 also includes one or more memories 302, 303. Memory302 may be random access memory (RAM). Memory 303 may include, forexample, a hard disk drive and/or a removable storage drive, such as afloppy disk drive, a magnetic tape drive, or an optical disk drive, byway of example. Removable storage drive reads from and/or writes to aremovable storage unit (e.g., a floppy disk, magnetic tape, opticaldisk, by way of example) as will be known to those skilled in the art.As will be understood by those skilled in the art, removable storageunit includes a computer usable storage medium having stored thereincomputer software and/or data.

In alternative implementations, memory 303 may include other similarmeans for allowing computer programs or other instructions to be loadedinto computer system 300. Such means may include, for example, aremovable storage unit and an interface. Examples of such means mayinclude a removable memory chip (such as an EPROM, or PROM, or flashmemory) and associated socket, and other removable storage units andinterfaces which allow software and data to be transferred fromremovable storage unit to computer system 300. Alternatively, theprogram may be executed and/or the data accessed from the removablestorage unit, using the processor 301 of the computer system 300.

Computer system 301 may also include a communication interface 304.Communication interface 304 allows software and data to be transferredbetween computer system 300 and external devices. Examples ofcommunication interface 304 may include a modem, a network interface(such as an Ethernet card), and a communication port, by way of example.Software and data transferred via communication interface 304 are in theform of signals, which may be electronic, electromagnetic, optical, orother signals capable of being received by communication interface 304.These signals are provided to communication interface 304 via acommunication path 305. Communication path 305 carries signals and maybe implemented using wire or cable, fiber optics, a phone line, awireless link, a cellular phone link, a radio frequency link, or anyother suitable communication channel, including a combination of theforegoing exemplary channels.

The terms “non-transitory computer readable medium”, “computer programmedium” and “computer usable medium” are used generally to refer tomedia such as removable storage drive, a hard disk installed in harddisk drive, and non-transitory signals, as described herein. Thesecomputer program products are means for providing software to computersystem 300. However, these terms may also include signals (such aselectrical, optical or electromagnetic signals) that embody the computerprogram disclosed herein.

Computer programs are stored in memory 302 and/or memory 303. Computerprograms may also be received via communication interface 304. Suchcomputer programs, when executed, enable computer system 300 toimplement the present invention as discussed herein. Accordingly, suchcomputer programs represent controllers of computer system 300. Wherethe invention is implemented using software, the software may be storedin a computer program product and loaded into computer system 300 usingremovable storage drive, hard disk drive, or communication interface304, to provide some examples.

It is necessary to carry out the methods of the present invention usinga computer. More particularly, the model simultaneously considers over2000 variables for each individual who contacts customer service todetermine if such individual should be referred to OCA. Moreparticularly, the predictive model assesses the relative similaritybetween a novel individual and the characteristics of individuals whohave behaved in a particular way (e.g., sought resolution for a problem)and experienced a particular outcome (e.g., non-resolution of theirproblem and escalation to OCA). An individual's likelihood to engage inan activity and experience a particular outcome is determined byconsidering the relationship between behaviors and outcomes over aperiod spanning multiple months with the aggregate data relating toparameters simultaneously existing in more than 2000 dimensions for eachof many thousands of individuals. Such processing could only be carriedout by a computer.

It will be appreciated by those skilled in the art that changes could bemade to the exemplary embodiments shown and described above withoutdeparting from the broad inventive concept thereof. It is understood,therefore, that this invention is not limited to the exemplaryembodiments shown and described, but it is intended to covermodifications within the spirit and scope of the present invention asdefined by the claims. For example, specific features of the exemplaryembodiments may or may not be part of the claimed invention and featuresof the disclosed embodiments may be combined. Unless specifically setforth herein, the terms “a”, “an” and “the” are not limited to oneelement but instead should be read as meaning “at least one”.

It is to be understood that at least some of the figures anddescriptions of the invention have been simplified to focus on elementsthat are relevant for a clear understanding of the invention, whileeliminating, for purposes of clarity, other elements that those ofordinary skill in the art will appreciate may also comprise a portion ofthe invention. However, because such elements are well known in the art,and because they do not necessarily facilitate a better understanding ofthe invention, a description of such elements is not provided herein.

Further, to the extent that the method does not rely on the particularorder of steps set forth herein, the particular order of the stepsshould not be construed as limitation on the claims. The claims directedto the method of the present invention should not be limited to theperformance of their steps in the order written, and one skilled in theart can readily appreciate that the steps may be varied and still remainwithin the spirit and scope of the present invention.

What is claimed is:
 1. A computer implemented method for predictingearly customer service intervention candidates, the method comprising:receiving household data for households that are associated with acustomer service interaction of an organization, which is received at afirst customer service computer terminal associated with a firstcustomer service level, as of a certain date, the household data beingcreated over a first pre-determined period of time preceding the certaindate and being stored in a database, wherein the first customer servicecomputer terminal is coupled to the database; isolating from thehousehold data, specific customer service interactions related to thehousehold data based on one or more of i) prior communications and ii)trigger mechanisms, to produce an isolated set of household data;training a support vector machine model using machine learning, thetraining including: identifying from the isolated set of household data,using the support vector machine model, positive household data setsrelating to customer service interactions, which preceded a high levelcustomer service interaction within a predetermined subsequent period oftime, and negative household data sets relating to customer serviceinteractions, which did not precede a high level customer serviceinteraction within the predetermined subsequent period of time, whereinthe identification of the positive household data sets and negativehousehold data sets are based on identifying patterns in customercharacteristics, qualitative characteristics obtained from unstructureddata, and quantitative characteristics obtained from structured data,wherein the identification includes uncovering patterns in case data,text analysis of unstructured data from call logs associated with atleast one of an emotional state of a customer, customer behavior, wordcount, and word usage; receiving, from the database, historicaltransactional data of the customer service interactions associated withthe positive household data sets, the historical transactional dataincluding data associated with transactions between the organization andhouseholds associated with the positive household data sets; andprocessing, using the support vector machine model, the positivehousehold data sets and the negative household data sets to determinedifferences between data contained within the positive household datasets and the negative household data sets based on at least thehistorical transactional data; collecting on a daily basis dailyhousehold data describing individual customer service interactionsoccurring during a previous calendar day; removing daily household datathat include households receiving an elevated interaction from theorganization based on a time period occurring in proximity to when theremoving occurred to generate filtered daily household data; processingthe filtered daily household data, using the support vector machinemodel, to determine whether each individual customer service interactionoccurring during the previous calendar day is more similar to thepositive household data sets or to the negative household data sets;flagging, using the support vector machine model, the individualcustomer service interactions that are more similar to the positivehousehold data sets based on at least the identified patterns in thecustomer characteristics; identifying, using the support vector machinemodel, elevated customer service level customer candidates for earlyintervention based upon the flagged individual customer serviceinteractions, wherein the elevated customer service level is differentfrom the first customer service level; displaying data associated withthe identified elevated customer service level customer candidates on auser interface to notify an end user that early intervention within apredetermined intervention time period is required; receiving anindication that early intervention has occurred and resulted inretention; receiving early intervention feedback data in response to theindication that the early intervention has occurred and resulted inretention; displaying on the user interface, an interaction elementassociated with the early intervention feedback data that allows the enduser to interact with the early intervention feedback data; andrecording, using the support vector machine model, the earlyintervention feedback data and refining the support vector machine modelbased on the recorded early intervention feedback data.
 2. The method ofclaim 1, wherein the differences are based on predetermined weightsassigned to each individual customer service interaction of the positivehousehold data sets and the negative household data sets.
 3. The methodof claim 1, wherein the unstructured data comprises textual data fromlog notes of customer service representatives.
 4. The method of claim 1,further comprising: displaying the flagged individual customer serviceinteractions, wherein upon displaying the flagged individual customerservice interactions, an end user is able to interact with the flaggedindividual customer service interactions.
 5. The method of claim 1further comprising: isolating from the individual customer serviceinteractions a subset including individual customer service interactionsthat have not preceded the high level customer service interactionwithin a predetermined amount of time.
 6. A system comprising: a memoryoperable to store at least one program; and a processor communicativelycoupled to the memory, the processor including a support vector machinemodel, in which the program, when executed by the processor, causes theprocessor to perform a method for predicting early customer serviceintervention candidates, the method comprising: receiving household datafor households that are associated with a customer service interactionof an organization, which is received at a first customer servicecomputer terminal associated with a first customer service level, as ofa certain date, the household data being created over a firstpre-determined period of time preceding the certain date and beingstored in a database, wherein the first customer service computerterminal is coupled to the database; isolating from the household dataspecific customer service interactions related to the household databased on one or more of i) prior communications and ii) triggermechanisms, to produce an isolated set of household data; training thesupport vector machine model using machine learning, the trainingincluding: identifying from the isolated set of household data, usingthe support vector machine model, positive household data sets relatingto customer service interactions, which preceded a high level customerservice interaction within a predetermined subsequent period of time,and negative household data sets relating to customer serviceinteractions, which did not precede a high level customer serviceinteraction within the predetermined subsequent period of time, whereinthe identification of the positive household data sets and negativehousehold data sets are based on identifying patterns in customercharacteristics, qualitative characteristics obtained from unstructureddata, and quantitative characteristics obtained from structured data,wherein the identification includes uncovering patterns in case data,text analysis of unstructured data from call logs associated with atleast one of an emotional state of a customer, customer behavior, wordcount, and word usage; receiving, from the database, historicaltransactional data of the customer service interactions associated withthe positive household data sets, the historical transactional dataincluding data associated with transactions between the organization andhouseholds associated with the positive household data sets; andprocessing, using the support vector machine model, the positivehousehold data sets and the negative household data sets to determinedifferences between data contained within the positive household datasets and the negative household data sets based on at least thehistorical transactional data; collecting on a daily basis dailyhousehold data describing individual customer service interactionsoccurring during a previous calendar day; removing daily household datathat include households receiving an elevated interaction from theorganization based on a time period occurring in proximity to when theremoving occurred to generate filtered daily household data; processingthe filtered daily household data, using the support vector machinemodel, to determine whether each individual customer service interactionoccurring during the previous calendar day is more similar to thepositive household data sets or to the negative household data sets;flagging, using the support vector machine model, the individualcustomer service interactions that are more similar to the positivehousehold data sets based on at least the identified patterns in thecustomer characteristics; identifying, using the support vector machinemodel, elevated customer service level customer candidates for earlyintervention based upon the flagged individual customer serviceinteractions, wherein the elevated customer service level is differentfrom the first customer service level; displaying data associated withthe identified elevated customer service level customer candidates on auser interface to notify an end user that early intervention within apredetermined intervention time period is required; receiving anindication that early intervention has occurred and resulted inretention; receiving early intervention feedback data in response to theindication that the early intervention has occurred and resulted inretention; displaying on the user interface, an interaction elementassociated with the early intervention feedback data that allows the enduser to interact with the early intervention feedback data; andrecording, using the support vector machine model, the earlyintervention feedback data and refining the support vector machine modelbased on the recorded early intervention feedback data.
 7. Anon-transitory computer readable medium storing instructions which, whenexecuted by a computer processor, including a trained support vectormachine model, cause the computer processor to perform a method forpredicting early customer service intervention candidates, the methodcomprising: receiving household data for households that are associatedwith a customer service interaction of an organization, which isreceived at a first customer service computer terminal associated with afirst customer service level, as of a certain date, the household databeing created over a first pre-determined period of time preceding thecertain date and being stored in a database, wherein the first customerservice computer terminal is coupled to the database; isolating from thehousehold data, specific customer service interactions related to thehousehold data based on one or more of i) prior communications and ii)trigger mechanisms, to produce an isolated set of household data;training a support vector machine model using machine learning, thetraining including: identifying from the isolated set of household data,using the support vector machine model, positive household data setsrelating to customer service interactions, which preceded a high levelcustomer service interaction within a predetermined subsequent period oftime, and negative household data sets relating to customer serviceinteractions, which did not precede a high level customer serviceinteraction within the predetermined subsequent period of time, whereinthe identification of the positive household data sets and negativehousehold data sets are based on identifying patterns in customercharacteristics, qualitative characteristics obtained from unstructureddata, and quantitative characteristics obtained from structured data,wherein the identification includes uncovering patterns in case data,text analysis of unstructured data from call logs associated with atleast one of an emotional state of a customer, customer behavior, wordcount, and word usage; receiving, from the database, historicaltransactional data of the customer service interactions associated withthe positive household data sets, the historical transactional dataincluding data associated with transactions between the organization andhouseholds associated with the positive household data sets; andprocessing, using the support vector machine model, the positivehousehold data sets and the negative household data sets to determinedifferences between data contained within the positive household datasets and the negative household data sets based on at least thehistorical transactional data; collecting on a daily basis dailyhousehold data describing individual customer service interactionsoccurring during a previous calendar day; removing daily household datathat include households receiving an elevated interaction from theorganization based on a time period occurring in proximity to when theremoving occurred to generate filtered daily household data; processingthe filtered daily household data, using the support vector machinemodel, to determine whether each individual customer service interactionoccurring during the previous calendar day is more similar to thepositive household data sets or to the negative household data sets;flagging, using the support vector machine model, the individualcustomer service interactions that are more similar to the positivehousehold data sets based on at least the identified patterns in thecustomer characteristics; identifying, using the support vector machinemodel, elevated customer service level customer candidates for earlyintervention based upon the flagged individual customer serviceinteractions, wherein the elevated customer service level is differentfrom the first customer service level; displaying data associated withthe identified elevated customer service level customer candidates on auser interface to notify an end user that early intervention within apredetermined intervention time period is required; receiving anindication that early intervention has occurred and resulted inretention; receiving early intervention feedback data in response to theindication that the early intervention has occurred and resulted inretention; displaying on the user interface, an interaction elementassociated with the early intervention feedback data that allows the enduser to interact with the early intervention feedback data; andrecording, using the support vector machine model, the earlyintervention feedback data and refining the support vector machine modelbased on the recorded early intervention feedback data.